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Moser, Lukas, Silvan Hess, Henrik Behrend, and Michael Hirschmann. "Variability of functional knee phenotypes in osteoarthritic knees shows that a more personalized approach in TKA is needed." Orthopaedic Journal of Sports Medicine 8, no. 5_suppl4 (May 1, 2020): 2325967120S0030. http://dx.doi.org/10.1177/2325967120s00300.
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Aims and Objectives: Recently, the functional knee phenotype concept was introduced as a new system to classify the coronal alignment of the lower limb. Until now, this concept has only been applied to non-osteoarthritic knees. The purpose of this study was therefore to phenotype osteoarthritic knees according to this concept and investigate the distribution of these phenotypes. Materials and Methods: Preoperative CT scans of osteoarthritic knees scheduled for TKA collected between January 2017 and December 2019 in the KneePLAN 3D database (Symbios Orthopédie S.A.) were reviewed for patients meeting the following inclusion criteria: age>50 and <90, no signs of previous fractures, osteotomies and rheumatoid arthritis. A total of 2764 patients (1438 right and 1326 left lower limbs, Male:female ratio 1096 :1668) with a mean age ± standard deviation of 70±8.5years (range 50-90 years) were included. The following coronal alignment parameters were measured using a validated software (KneePLAN 3D, Symbios Orthopédie S.A): hip-knee-ankle angle (HKA), femoral mechanical angle (FMA), and tibial mechanical angle (TMA). Based on these measurements each leg was phenotyped according to the functional knee phenotype concept and the distribution of these phenotypes assessed. A phenotype thereby consists of a phenotype specific mean value (HKA, FMA or TMA value) and covers a range of ± 1.5° from this mean (e.g. 180°± 1.5). The phenotype specific mean values represent 3° increments of the angle starting from the rounded overall mean value of the angle. Results: There were 162 different functional knee phenotypes (122 male, 138 female and 97 mutual). The most common functional knee phenotype in males was VARHKA6°VARFMA3°NEUTMA0° accounting for 8% of all males. The most common functional knee phenotype in females was VARHKA3°NEUFMA0°NEUTMA0° accounting for 9% of the population. The ten most common functional phenotypes account for 50% and 42.8% of all females and males, respectively. Overall, 134 phenotypes accounted each for less than 1% of the total population (all 134 together for 26.4%). Conclusion: The broad variability of functional knee phenotypes in osteoarthritic knees shows that a more personalized TKA realignment strategy is needed. The challenge will be to identify the optimal alignment strategy for each functional knee phenotype.
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Roemer, F., J. Collins, T. Neogi, M. Crema, and A. Guermazi. "FRI0421 RATES OF PROGRESSION DIFFER BETWEEN STRUCTURAL PHENOTYPES OF KNEE OSTEOARTHRITIS: A SECONDARY ANALYSIS FROM THE FNIH COHORT." Annals of the Rheumatic Diseases 79, Suppl 1 (June 2020): 808.1–809. http://dx.doi.org/10.1136/annrheumdis-2020-eular.1802.
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Background:Imaging plays an important role in determining structural disease severity and potential suitability of patients recruited to disease-modifying osteoarthritis drug (DMOAD) trials. It has been suggested that there may be three main structural phenotypes in OA, i.e., inflammation, meniscus/cartilage and subchondral bone. These may progress differently and may represent distinct tissue targets for DMOAD approaches.Objectives:To stratify the Foundation for National Institutes of Health Osteoarthritis Biomarkers Consortium (FNIH) cohort, a well-defined subsample of the larger Osteoarthritis Initiative (OAI) study, into distinct structural phenotypes based on semiquantitative MRI assessment and to determine their risk for progression over 48 months.Methods:The FNIH was designed as a case-control study with knees showing either 1) radiographic and pain progression (i.e., “composite” cases), 2) radiographic progression only (“JSL”), 3) pain progression only, and 4) neither radiographic nor pain progression. MRI of both knees was performed on 3 T systems at the four OAI clinical sites. Two musculoskeletal radiologists read the baseline MRIs according to the MOAKS scoring system. Knees were stratified into subchondral bone, meniscus/cartilage and inflammatory phenotypes1. A secondary, less stringent definition for inflammatory and meniscus/cartilage phenotype was used for sensitivity analyses. The relation of each phenotype to risk of being in the JSL or composite case group compared to those not having that phenotype was determined using conditional logistic regression. Only KL2 and 3 and those without root tears were included.Results:485 knees were included. 362 (75%) did not have any phenotype, while 95 (20%) had the bone phenotype, 22 (5%) the cartilage/meniscus phenotype and 19 (4%) the inflammatory phenotype. The bone phenotype was associated with a higher risk of the JSL and composite outcome (OR 1.81;[95%CI 1.14,2.85] and 1.65; 95%CI [1.04,2.61]) while the inflammatory (OR 0.96 [95%CI 0.38,2.42] and 1.25; 95%CI [0.48,3.25]) and the meniscus/cartilage phenotypes were not (OR 1.30 95%CI [0.55,3.07] and 0.99; 95%CI [0.40,2,49]).In sensitivity analyses, the bone phenotype and having two phenotypes (vs. none) were both associated with increased risk of experiencing the composite outcome (bone: OR 1.65; 95% CI 1.04, 2.61; 2 phenotypes: OR 1.87; 95% CI 1.11, 3.16.Conclusion:The bone phenotype was associated with increased risk of having both radiographic and pain progression together, or radiographic progression alone, whereas the inflammatory phenotype or meniscus/cartilage phenotype each individually were not associated with either outcome. Phenotypic stratification appears to provide insights into risk for structural or composite structure plus pain progression, and therefore may be useful to consider when selecting patients for inclusion in clinical trials.References:[1]Roemer FW, Collins J, Kwoh CK, et al. MRI-based screening for structural definition of eligibility in clinical DMOAD trials: Rapid OsteoArthritis MRI Eligibility Score (ROAMES). Osteoarthritis Cartilage 2020;28(1):71-81Disclosure of Interests:Frank Roemer: None declared, Jamie Collins Consultant of: Boston Imaging Core Lab (BICL), LLC., Tuhina Neogi Grant/research support from: Pfizer/Lilly, Consultant of: Pfizer/Lilly, EMD-Merck Serono, Novartis, Michel Crema: None declared, Ali Guermazi Consultant of: AventisGalapagos, Pfizer, Roche, AstraZeneca, Merck Serono, and TissuGene
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Carmina, Enrico, and Rogerio A. Lobo. "Comparing Lean and Obese PCOS in Different PCOS Phenotypes: Evidence That the Body Weight Is More Important Than the Rotterdam Phenotype in Influencing the Metabolic Status." Diagnostics 12, no. 10 (September 25, 2022): 2313. http://dx.doi.org/10.3390/diagnostics12102313.
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Polycystic Ovary Syndrome (PCOS) represents a heterogeneous disorder and, using Rotterdam diagnostic criteria, four main phenotypes (A, B, C, and D) have been distinguished. However, it remains unclear whether lean versus obesity status influences findings in the various phenotypes of women with PCOS. 274 women with PCOS were consecutively assessed. Among these women, there were 149 with phenotype A, 24 with phenotype B, 94 with phenotype C, and 7 with phenotype D. We found normal body weight to be very common (65%) in phenotype C patients, common (43%) in phenotype A and D patients, and less represented (but still 25%) in phenotype B patients. Obesity was common in phenotype B (54%) and phenotype A (33%) patients and uncommon in phenotype C (only 11%) and phenotype D (14%) patients. Obese and lean patients of each phenotype were compared. Compared to the phenotype C PCOS patients, both phenotype A and B patients had higher total testosterone circulating values and higher luteinizing hormone/follicle stimulating hormone (LH/FSH) ratio (p < 0.01) while anti-Mullerian hormone (AMH) levels were higher only in phenotype A PCOS patients. Instead, in the three obese PCOS phenotypes no differences in serum insulin, Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) calculation, and lipid blood values were observed. Analysis of data of lean patients gave similar results. Compared to the phenotype C PCOS patients, both phenotype A and B patients had higher total testosterone circulating values and higher LH/FSH ratio (p < 0.01) while AMH levels were higher only in phenotype A PCOS patients. However, no differences were observed in the circulating insulin levels, HOMA-IR calculation, or blood lipids between the three groups of lean PCOS patients. We conclude that Rotterdam phenotypes express the differences between PCOS patients in terms of ovulatory pattern and androgen secretion but fail to differentiate between obese patients with altered metabolic patterns and lean patients with normal metabolic patterns. A new classification of PCOS patients is needed and it should consider the influence of body weight on the metabolic patterns of PCOS patients.
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Postoeva, A. V., I. V. Dvoryashina, A. V. Kudryavtsev, and V. A. Postoev. "Prevalence of metabolic phenotypes among citizens of Arctic area of the Russian Federation (in Arkhangelsk city setting)." Obesity and metabolism 20, no. 1 (May 22, 2023): 34–42. http://dx.doi.org/10.14341/omet12926.
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BACKGROUND: Influence of obesity on the body at whole and with regard to metabolic changes is still unclear. In Russia there are a few data about prevalence of metabolic phenotypes among population based on epidemiological data.AIM: to assess the prevalence of metabolic phenotypes among citizens of Arctic area of the Russian Federation (in the Arkhangelsk city setting).MATERIALS AND METHODS: a cross-sectional study was conducted using a random sample of Arkhangelsk citizens (n=2380) 35–69 years old, which was obtained within a population study of cardiovascular diseases («Know your heart» (KYH)). The participants were divided into metabolic phenotypes according to the presence of obesity (BMI≥30 kg/m2) and metabolic syndrome (AHA/NHBLI): phenotype 1 — metabolically healthy normal weight, phenotype 2 — metabolically unhealthy normal weight, phenotype 3 — metabolically healthy obesity, phenotype 4 — metabolically unhealthy obesity.RESULTS: 2352 participants of KYH were included in the study, 982 (41,8%) men and 1370 (58,3%) women. Mean age was 53,9 (SD 9,7) years. The distribution of participants by metabolic phenotypes was as follows: 1167 (49,6%) persons had phenotype 1, 489 (20,8%) — phenotype 2, 248 (10,5%) — phenotype 3, 448 (19,1%) — phenotype 4. In men, the second common after the first phenotype was phenotype 2, while in women, the second position was shared by the 2nd and 4th phenotypes, which had approximately the same frequency. «Arterial hypertension» was the most prevalent component of metabolic syndrome and seen in 68–96% men and 38–94% women in the study with different phenotypes. The proportions of phenotypes with metabolic disorders increased with age.CONCLUSION: in a study of a random population sample within the framework of the concept of metabolic phenotypes, a half of the participants had no obesity and metabolic syndrome. Proportions of participants with metabolic disorders with and without obesity was 20% each. Only 10% of participants had «metabolically healthy» obesity. If excluding individuals without obesity and metabolic syndrome, the phenotype characterized by metabolic disorders in the absence of obesity was the most common among men. Phenotypes with metabolic disorders on the background of obesity or without obesity were equally common among women. The most common component of metabolic syndrome was «arterial hypertension». There was a tendency of accumulation of metabolic disturbances with age.
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Lusczek, Elizabeth R., Nicholas E. Ingraham, Basil S. Karam, Jennifer Proper, Lianne Siegel, Erika S. Helgeson, Sahar Lotfi-Emran, et al. "Characterizing COVID-19 clinical phenotypes and associated comorbidities and complication profiles." PLOS ONE 16, no. 3 (March 31, 2021): e0248956. http://dx.doi.org/10.1371/journal.pone.0248956.
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PurposeHeterogeneity has been observed in outcomes of hospitalized patients with coronavirus disease 2019 (COVID-19). Identification of clinical phenotypes may facilitate tailored therapy and improve outcomes. The purpose of this study is to identify specific clinical phenotypes across COVID-19 patients and compare admission characteristics and outcomes.MethodsThis is a retrospective analysis of COVID-19 patients from March 7, 2020 to August 25, 2020 at 14 U.S. hospitals. Ensemble clustering was performed on 33 variables collected within 72 hours of admission. Principal component analysis was performed to visualize variable contributions to clustering. Multinomial regression models were fit to compare patient comorbidities across phenotypes. Multivariable models were fit to estimate associations between phenotype and in-hospital complications and clinical outcomes.ResultsThe database included 1,022 hospitalized patients with COVID-19. Three clinical phenotypes were identified (I, II, III), with 236 [23.1%] patients in phenotype I, 613 [60%] patients in phenotype II, and 173 [16.9%] patients in phenotype III. Patients with respiratory comorbidities were most commonly phenotype III (p = 0.002), while patients with hematologic, renal, and cardiac (all p<0.001) comorbidities were most commonly phenotype I. Adjusted odds of respiratory, renal, hepatic, metabolic (all p<0.001), and hematological (p = 0.02) complications were highest for phenotype I. Phenotypes I and II were associated with 7.30-fold (HR:7.30, 95% CI:(3.11–17.17), p<0.001) and 2.57-fold (HR:2.57, 95% CI:(1.10–6.00), p = 0.03) increases in hazard of death relative to phenotype III.ConclusionWe identified three clinical COVID-19 phenotypes, reflecting patient populations with different comorbidities, complications, and clinical outcomes. Future research is needed to determine the utility of these phenotypes in clinical practice and trial design.
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de Koning-Tijssen, M. "One gene many phenotypes, one phenotype many genes." Journal of the Neurological Sciences 405 (October 2019): 11. http://dx.doi.org/10.1016/j.jns.2019.10.028.
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Xiromerisiou, Georgia, Henry Houlden, Nikolaos Scarmeas, Maria Stamelou, Eleanna Kara, John Hardy, Andrew J. Lees, et al. "THAP1 mutations and dystonia phenotypes: Genotype phenotype correlations." Movement Disorders 27, no. 10 (August 17, 2012): 1290–94. http://dx.doi.org/10.1002/mds.25146.
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Ferguson, Amy Christina, Sophie Thrippleton, David Henshall, Ed Whittaker, Bryan Conway, Malcolm MacLeod, Rainer Malik, et al. "Frequency and Phenotype Associations of Rare Variants in 5 Monogenic Cerebral Small Vessel Disease Genes in 200,000 UK Biobank Participants." Neurology Genetics 8, no. 5 (August 24, 2022): e200015. http://dx.doi.org/10.1212/nxg.0000000000200015.
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Background and ObjectivesBased on previous case reports and disease-based cohorts, a minority of patients with cerebral small vessel disease (cSVD) have a monogenic cause, with many also manifesting extracerebral phenotypes. We investigated the frequency, penetrance, and phenotype associations of putative pathogenic variants in cSVD genes in the UK Biobank (UKB), a large population-based study.MethodsWe used a systematic review of previous literature and ClinVar to identify putative pathogenic rare variants in CTSA, TREX1, HTRA1, and COL4A1/2. We mapped phenotypes previously attributed to these variants (phenotypes-of-interest) to disease coding systems used in the UKB's linked health data from UK hospital admissions, death records, and primary care. Among 199,313 exome-sequenced UKB participants, we assessed the following: the proportion of participants carrying ≥1 variant(s); phenotype-of-interest penetrance; and the association between variant carrier status and phenotypes-of-interest using a binary (any phenotype present/absent) and phenotype burden (linear score of the number of phenotypes a participant possessed) approach.ResultsAmong UKB participants, 0.5% had ≥1 variant(s) in studied genes. Using hospital admission and death records, 4%–20% of variant carriers per gene had an associated phenotype. This increased to 7%–55% when including primary care records. Only COL4A1 variant carrier status was significantly associated with having ≥1 phenotype-of-interest and a higher phenotype score (OR = 1.29, p = 0.006).DiscussionWhile putative pathogenic rare variants in monogenic cSVD genes occur in 1:200 people in the UKB population, only approximately half of variant carriers have a relevant disease phenotype recorded in their linked health data. We could not replicate most previously reported gene-phenotype associations, suggesting lower penetrance rates, overestimated pathogenicity, and/or limited statistical power.
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Zhou, Xue, Keijiro Nakamura, Naohiko Sahara, Masako Asami, Yasutake Toyoda, Yoshinari Enomoto, Hidehiko Hara, et al. "Exploring and Identifying Prognostic Phenotypes of Patients with Heart Failure Guided by Explainable Machine Learning." Life 12, no. 6 (May 24, 2022): 776. http://dx.doi.org/10.3390/life12060776.
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Identifying patient prognostic phenotypes facilitates precision medicine. This study aimed to explore phenotypes of patients with heart failure (HF) corresponding to prognostic condition (risk of mortality) and identify the phenotype of new patients by machine learning (ML). A unsupervised ML was applied to explore phenotypes of patients in a derivation dataset (n = 562) based on their medical records. Thereafter, supervised ML models were trained on the derivation dataset to classify these identified phenotypes. Then, the trained classifiers were further validated on an independent validation dataset (n = 168). Finally, Shapley additive explanations were used to interpret decision making of phenotype classification. Three patient phenotypes corresponding to stratified mortality risk (high, low, and intermediate) were identified. Kaplan–Meier survival curves among the three phenotypes had significant difference (pairwise comparison p < 0.05). Hazard ratio of all-cause mortality between patients in phenotype 1 (n = 91; high risk) and phenotype 3 (n = 329; intermediate risk) was 2.08 (95%CI 1.29–3.37, p = 0.003), and 0.26 (95%CI 0.11–0.61, p = 0.002) between phenotype 2 (n = 142; low risk) and phenotype 3. For phenotypes classification by random forest, AUCs of phenotypes 1, 2, and 3 were 0.736 ± 0.038, 0.815 ± 0.035, and 0.721 ± 0.03, respectively, slightly better than the decision tree. Then, the classifier effectively identified the phenotypes for new patients in the validation dataset with significant difference on survival curves and hazard ratios. Finally, age and creatinine clearance rate were identified as the top two most important predictors. ML could effectively identify patient prognostic phenotypes, facilitating reasonable management and treatment considering prognostic condition.
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Spring, Michele D., Jason C. Sousa, Qigui Li, Christian A. Darko, Meshell N. Morrison, Sean R. Marcsisin, Kristin T. Mills, et al. "Determination of Cytochrome P450 Isoenzyme 2D6 (CYP2D6) Genotypes and Pharmacogenomic Impact on Primaquine Metabolism in an Active-Duty US Military Population." Journal of Infectious Diseases 220, no. 11 (September 24, 2019): 1761–70. http://dx.doi.org/10.1093/infdis/jiz386.
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Abstract Background Plasmodium vivax malaria requires a 2-week course of primaquine (PQ) for radical cure. Evidence suggests that the hepatic isoenzyme cytochrome P450 2D6 (CYP2D6) is the key enzyme required to convert PQ into its active metabolite. Methods CYP2D6 genotypes and phenotypes of 550 service personnel were determined, and the pharmacokinetics (PK) of a 30-mg oral dose of PQ was measured in 45 volunteers. Blood and urine samples were collected, with PQ and metabolites were measured using ultraperformance liquid chromatography with mass spectrometry. Results Seventy-six CYP2D6 genotypes were characterized for 530 service personnel. Of the 515 personnel for whom a single phenotype was predicted, 58% had a normal metabolizer (NM) phenotype, 35% had an intermediate metabolizer (IM) phenotype, 5% had a poor metabolizer (PM) phenotype, and 2% had an ultrametabolizer phenotype. The median PQ area under the concentration time curve from 0 to ∞ was lower for the NM phenotype as compared to the IM or PM phenotypes. The novel 5,6-ortho-quinone was detected in urine but not plasma from all personnel with the NM phenotype. Conclusion The plasma PK profile suggests PQ metabolism is decreased in personnel with the IM or PM phenotypes as compared to those with the NM phenotype. The finding of 5,6-ortho-quinone, the stable surrogate for the unstable 5-hydroxyprimaquine metabolite, almost exclusively in personnel with the NM phenotype, compared with sporadic or no production in those with the IM or PM phenotypes, provides further evidence for the role of CYP2D6 in radical cure. Clinical Trials Registration NCT02960568.
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Cao, Xuewei, Shuanglin Zhang, and Qiuying Sha. "A novel method for multiple phenotype association studies based on genotype and phenotype network." PLOS Genetics 20, no. 5 (May 10, 2024): e1011245. http://dx.doi.org/10.1371/journal.pgen.1011245.
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Joint analysis of multiple correlated phenotypes for genome-wide association studies (GWAS) can identify and interpret pleiotropic loci which are essential to understand pleiotropy in diseases and complex traits. Meanwhile, constructing a network based on associations between phenotypes and genotypes provides a new insight to analyze multiple phenotypes, which can explore whether phenotypes and genotypes might be related to each other at a higher level of cellular and organismal organization. In this paper, we first develop a bipartite signed network by linking phenotypes and genotypes into a Genotype and Phenotype Network (GPN). The GPN can be constructed by a mixture of quantitative and qualitative phenotypes and is applicable to binary phenotypes with extremely unbalanced case-control ratios in large-scale biobank datasets. We then apply a powerful community detection method to partition phenotypes into disjoint network modules based on GPN. Finally, we jointly test the association between multiple phenotypes in a network module and a single nucleotide polymorphism (SNP). Simulations and analyses of 72 complex traits in the UK Biobank show that multiple phenotype association tests based on network modules detected by GPN are much more powerful than those without considering network modules. The newly proposed GPN provides a new insight to investigate the genetic architecture among different types of phenotypes. Multiple phenotypes association studies based on GPN are improved by incorporating the genetic information into the phenotype clustering. Notably, it might broaden the understanding of genetic architecture that exists between diagnoses, genes, and pleiotropy.
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Amado, Manuella Villar, Izeni P. Farias, and Tomas Hrbek. "A Molecular Perspective on Systematics, Taxonomy and Classification Amazonian Discus Fishes of the Genus Symphysodon." International Journal of Evolutionary Biology 2011 (July 28, 2011): 1–16. http://dx.doi.org/10.4061/2011/360654.
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With the goal of contributing to the taxonomy and systematics of the Neotropical cichlid fishes of the genus Symphysodon, we analyzed 336 individuals from 24 localities throughout the entire distributional range of the genus. We analyzed variation at 13 nuclear microsatellite markers, and subjected the data to Bayesian analysis of genetic structure. The results indicate that Symphysodon is composed of four genetic groups: group PURPLE—phenotype Heckel and abacaxi; group GREEN—phenotype green; group RED—phenotype blue and brown; and group PINK—populations of Xingú and Cametá. Although the phenotypes blue and brown are predominantly biological group RED, they also have substantial contributions from other biological groups, and the patterns of admixture of the two phenotypes are different. The two phenotypes are further characterized by distinct and divergent mtDNA haplotype groups, and show differences in mean habitat use measured as pH and conductivity. Differences in mean habitat use is also observed between most other biological groups. We therefore conclude that Symphysodon comprises five evolutionary significant units: Symphysodon discus (Heckel and abacaxi phenotypes), S. aequifasciatus (brown phenotype), S. tarzoo (green phenotype), Symphysodon sp. 1 (blue phenotype) and Symphysodon sp. 2 (Xingú group).
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Majewski, Sebastian, Maciej Ciebiada, Mateusz Domagala, Zofia Kurmanowska, and Pawel Gorski. "Short-Term Reproducibility of the Inflammatory Phenotype in Different Subgroups of Adult Asthma Cohort." Mediators of Inflammation 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/419039.
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Inflammatory phenotype classification using induced sputum appears attractive as it can be applied to inflammation-based management of the patients with asthma. The aim of the study was to determine the reproducibility of inflammatory phenotype over time in patients with asthma. In 66 adults asthma was categorized as steroid-naïve (SN,n=17), mild to moderate (MMA,n=33), and refractory treated with oral corticosteroids (RA,n=16). Clinical assessment, skin prick testing, spirometry, and two sputum inductions in 4–6-week interval were done. Inflammatory phenotypes were classified as eosinophilic (EA), consisting of eosinophilic and mixed granulocytic phenotypes, and noneosinophilic (NEA) consisting of paucigranulocytic and neutrophilic phenotypes. During study asthma treatment remained constant. In SN group 25% of patients changed phenotype from EA to NEA and 44% changed phenotype from NEA to EA. In MMA group 26% of patients changed phenotype from EA to NEA and 50% changed phenotype from NEA to EA. In 29% of RA patients inflammatory phenotype changed from EA to NEA and in 22% it changed from NEA to EA. Inflammatory classification, using induced sputum, is not fully reproducible in adults with asthma in short-term evaluation. EA seems to be more stable phenotype across all subgroups whereas NEA remained stable only in RA group.
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Schupp, Jonas Christian, Sandra Freitag-Wolf, Elena Bargagli, Violeta Mihailović-Vučinić, Paola Rottoli, Aleksandar Grubanovic, Annegret Müller, et al. "Phenotypes of organ involvement in sarcoidosis." European Respiratory Journal 51, no. 1 (January 2018): 1700991. http://dx.doi.org/10.1183/13993003.00991-2017.
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Sarcoidosis is a highly variable, systemic granulomatous disease of hitherto unknown aetiology. The GenPhenReSa (Genotype–Phenotype Relationship in Sarcoidosis) project represents a European multicentre study to investigate the influence of genotype on disease phenotypes in sarcoidosis.The baseline phenotype module of GenPhenReSa comprised 2163 Caucasian patients with sarcoidosis who were phenotyped at 31 study centres according to a standardised protocol.From this module, we found that patients with acute onset were mainly female, young and of Scadding type I or II. Female patients showed a significantly higher frequency of eye and skin involvement, and complained more of fatigue. Based on multidimensional correspondence analysis and subsequent cluster analysis, patients could be clearly stratified into five distinct, yet undescribed, subgroups according to predominant organ involvement: 1) abdominal organ involvement, 2) ocular–cardiac–cutaneous–central nervous system disease involvement, 3) musculoskeletal–cutaneous involvement, 4) pulmonary and intrathoracic lymph node involvement, and 5) extrapulmonary involvement.These five new clinical phenotypes will be useful to recruit homogenous cohorts in future biomedical studies.
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Ageev, F. T., and A. G. Ovchinnikov. "Treatment of patients with heart failure and preserved ejection fraction: reliance on clinical phenotypes." Kardiologiia 62, no. 7 (July 31, 2022): 44–53. http://dx.doi.org/10.18087/cardio.2022.7.n2058.
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The article discusses the problem of improving the effectiveness of treatment of heart failure with preserved left ventricular ejection fraction (HFpEF). The relative "failure" of early studies with renin-angiotensin-aldosterone system inhibitors was largely due to the lack of understanding that patients with HFpEF represent a heterogeneous group with various etiological factors and pathogenetic mechanisms of the disease. Therefore, the so-called personalized approach should be used in the treatment of these patients. This approach is based on the identification of clearly defined disease phenotypes, each characterized by a set of demographic, pathogenetic, and clinical characteristics. Based on the literature and own experience, the authors consider four main phenotypes of HFpEF: 1) phenotype with brain natriuretic peptide “deficiency” syndrome associated with moderate/severe left ventricular hypertrophy; 2) cardiometabolic phenotype; 3) phenotype with mixed pulmonary hypertension and right ventricular failure; and 4) cardiac amyloidosis phenotype. In the treatment of patients with phenotype 1, it seems preferable to use the valsartan + sacubitril (possibly in combination with spironolactone) combination treatment; with phenotype 2, the empagliflozin treatment is the best; with phenotype 3, the phosphodiesterase type 5 inhibitor sildenafil; and with phenotype 4, transthyretin stabilizers. Certain features of different phenotypes overlap and may change as the disease progresses. Nevertheless, the isolation of these phenotypes is advisable to prioritize the choice of drug therapy. Thus, the diuretic treatment (preferably torasemide) should be considered in the presence of congestion, regardless of the HFpEF phenotype; the valsartan + sacubitril and spironolactone treatment is appropriate not only in the shortage of brain natriuretic peptide but also in the presence of concentric left ventricular hypertrophy (except for the amyloidosis phenotype); and the treatment with empagliflozin and statins may be considered in all situations where pro-inflammatory mechanisms are involved.
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Darmency, Henri, and Catherine Aujas. "Genetic Diversity for Competitive and Reproductive Ability in Wild Oats (Avena fatua)." Weed Science 40, no. 2 (June 1992): 215–19. http://dx.doi.org/10.1017/s0043174500057258.
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Three wild oats phenotypes were grown in wheat stands sown at different dates in greenhouse and field trials. Wild oats growth and seed output, and their effects on wheat biomass were not different among phenotypes when wild oats emerged 2 wk after the wheat. In experiments in which wild oats were planted in germinated wheat, one phenotype was shorter, weighed less, and produced fewer seed than the other phenotypes. Another phenotype reduced wheat biomass more than the other phenotypes. Vernalization increased vegetative growth and reduced spikelet production of one phenotype, but had no effect on its competitiveness with wheat.
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Polak, Aleksandra Maria, Agnieszka Adamska, Anna Krentowska, Agnieszka Łebkowska, Justyna Hryniewicka, Marcin Adamski, and Irina Kowalska. "Body Composition, Serum Concentrations of Androgens and Insulin Resistance in Different Polycystic Ovary Syndrome Phenotypes." Journal of Clinical Medicine 9, no. 3 (March 9, 2020): 732. http://dx.doi.org/10.3390/jcm9030732.
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Insulin resistance and hyperandrogenemia observed in polycystic ovary syndrome (PCOS) are associated with metabolic disturbances and could be connected with body composition pattern. To date, several studies defining the parameters of body composition using dual energy X-ray absorptiometry (DXA) method in the group of PCOS patients have been published, however, without the analysis in different phenotypes. The aim of the present study was to investigate the relationships between serum androgens concentration, insulin resistance and distribution of fat mass using DXA method in various PCOS phenotypes according to the Rotterdam criteria. We examined 146 women: 34 (38%) had PCOS phenotype A, 20 (23%) phenotype B, 20 (23%) phenotype C and 15 (16%) phenotype D (with mean age of each phenotype 25 years), and 57 control subjects (mean age of 25.5 years). Homeostasis model assessment of insulin resistance (HOMA-IR) was calculated. Serum concentrations of testosterone, androstenedione and dehydroepiandrosterone sulfate (DHEA-S) were assessed and free androgen index (FAI) was calculated. In phenotypes A, B and C, we observed higher FAI in comparison to the control group (all p < 0.01). Serum concentrations of androstenedione and DHEA-S were higher in phenotypes A and C in comparison to the control group (all p < 0.01). However, only in phenotype A we found higher visceral adipose tissue (VAT) mass and android/gynoid ratio (A/G ratio) in comparison to the control group (all p < 0.01). In phenotype A, we observed connection of VAT with FAI (r = 0.58, p < 0.01). Accordingly, A/G ratio was related with FAI in all phenotypes (all p < 0.05). Additionally, in phenotype C, A/G ratio was related to serum concentrations of DHEA-S and androstenedione (r = 0.46, p = 0.03; r = 0.53, p = 0.01, respectively). We also found connections of HOMA-IR with VAT and A/G ratio in all phenotypes (all p < 0.05). Women with phenotype A had higher amount of VAT and A/G ratio in comparison to the control group. Serum concentration of androgens and insulin resistance are connected with VAT and A/G ratio in normoandrogenic and hyperandrogenic PCOS phenotypes.
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Deng, Yamin, Shiman Wu, and Huifang Fan. "Genome-wide pathway-based quantitative multiple phenotypes analysis." PLOS ONE 15, no. 11 (November 11, 2020): e0240910. http://dx.doi.org/10.1371/journal.pone.0240910.
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For complex diseases, genome-wide pathway association studies have become increasingly promising. Currently, however, pathway-based association analysis mainly focus on a single phenotype, which may insufficient to describe the complex diseases and physiological processes. This work proposes a combination model to evaluate the association between a pathway and multiple phenotypes and to reduce the run time based on asymptotic results. For a single phenotype, we propose a semi-supervised maximum kernel-based U-statistics (mSKU) method to assess the pathway-based association analysis. For multiple phenotypes, we propose the fisher combination function with dependent phenotypes (FC) to transform the p-values between the pathway and each marginal phenotype individually to achieve pathway-based multiple phenotypes analysis. With real data from the Alzheimer Disease Neuroimaging Initiative (ADNI) study and Human Liver Cohort (HLC) study, the FC-mSKU method allows us to specify which pathways are specific to a single phenotype or contribute to common genetic constructions of multiple phenotypes. If we only focus on single-phenotype tests, we may miss some findings for etiology studies. Through extensive simulation studies, the FC-mSKU method demonstrates its advantages compared with its counterparts.
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Yourman, L. F., S. N. Jeffers, and R. A. Dean. "Phenotype Instability in Botrytis cinerea in the Absence of Benzimidazole and Dicarboximide Fungicides." Phytopathology® 91, no. 3 (March 2001): 307–15. http://dx.doi.org/10.1094/phyto.2001.91.3.307.
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Stability of phenotypes of isolates of Botrytis cinerea that were sensitive or resistant to benzimidazole and dicarboximide fungicides was examined in the absence of fungicides in laboratory and growth room experiments. Twelve greenhouse isolates of B. cinerea were subcultured on potato dextrose agar (PDA) for 20 generations and on geranium seedlings for 15 generations. Three isolates of each of the following four phenotypes were used: sensitive to the fungicides thiophanate-methy1 (a benzimidazole) and vinclozolin (a dicarboximide) (STSV), resistant to both fungicides (RTRV), resistant to thiophanate-methy1 and sensitive to vinclozolin (RTSV), and sensitive to thiophanate-methy1 and resistant to vinclozolin (STRV). In three trials on PDA, 36 populations were subcultured; 8 populations changed phenotypes by the end of 20 generations, as determined by conidium germination on fungicide-amended medium. Five of the eight initially were STRV; the resulting phenotypes were STSV, RTSV, and RTRV. Populations from eight other isolates exhibited temporary changes in phenotype during intermediate generations on PDA but reverted to initial phenotypes by the twentieth generation; five of these populations changed to phenotype RTRV. In two geranium seedling trials, each of the 12 greenhouse isolates was inoculated onto a set of three seedlings for each generation, and diseased tissue that developed was used to initiate the next generation. Therefore, a total of 72 populations of B. cinerea were subcultured in the two trials; 5 of these populations changed phenotype at the end of 15 generations. Three of the five initially were STRV; these changed to phenotypes STSV or RTRV. In each of the two trials on geranium seedlings, a population subcultured from one STSV isolate changed phenotype one to phenotype RTRV and one to phenotype RTSV. In all trials, no population resistant to thiophanate-methy1 changed to a thiophanate-methy1-sensitive phenotype, and no population changed to phenotype STRV. Random amplified polymorphic DNA (RAPD) fingerprints were generated with the 12 initial isolates and 49 isolates subcultured on PDA or geranium seedlings. Cluster analyses of RAPD markers showed that subcultured isolates exhibiting the same phenotype clustered together and that subcultured isolates derived from a common greenhouse isolate but with different phenotypes were in different clusters. Some populations that did not change phenotype exhibited considerable differences in RAPD marker patterns. The results of this study indicate that, in the absence of fungicides, sensitive populations of B. cinerea can develop resistance to thiophanate-methy1 and vinclozolin, and this resistance can be maintained in populations through multiple generations. Populations resistant only to vinclozolin (STRV) exhibited a high frequency of phenotype change, and populations resistant to both fungicides (RTRV) were stable.
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Nguyen, Ngoc-Thanh-Van, Hoai-An Nguyen, Hai Hoang Nguyen, Binh Quang Truong, and Hoa Ngoc Chau. "Phenotype-Specific Outcome and Treatment Response in Heart Failure with Preserved Ejection Fraction with Comorbid Hypertension and Diabetes: A 12-Month Multicentered Prospective Cohort Study." Journal of Personalized Medicine 13, no. 8 (July 31, 2023): 1218. http://dx.doi.org/10.3390/jpm13081218.
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Despite evidence of SGLT2 inhibitors in improving cardiovascular outcomes of heart failure with preserved ejection fraction (HFpEF), the heterogenous mechanism and characteristic multimorbidity of HFpEF require a phenotypic approach. Metabolic phenotype, one common HFpEF phenotype, has various presentations and prognoses worldwide. We aimed to identify different phenotypes of hypertensive-diabetic HFpEF, their phenotype-related outcomes, and treatment responses. The primary endpoint was time to the first event of all-cause mortality or hospitalization for heart failure (HHF). Among 233 recruited patients, 24.9% experienced primary outcomes within 12 months. A total of 3.9% was lost to follow-up. Three phenotypes were identified. Phenotype 1 (n = 126) consisted of lean, elderly females with chronic kidney disease, anemia, and concentric hypertrophy. Phenotype 2 (n = 62) included younger males with coronary artery disease. Phenotype 3 (n = 45) comprised of obese elderly with atrial fibrillation. Phenotype 1 and 2 reported higher primary outcomes than phenotype 3 (p = 0.002). Regarding treatment responses, SGLT2 inhibitor was associated with fewer primary endpoints in phenotype 1 (p = 0.003) and 2 (p = 0.001). RAAS inhibitor was associated with fewer all-cause mortality in phenotype 1 (p = 0.003). Beta blocker was associated with fewer all-cause mortality in phenotype 1 (p = 0.024) and fewer HHF in phenotype 2 (p = 0.011). Our pioneering study supports the personalized approach to optimize HFpEF management in hypertensive-diabetic patients.
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Wang, Gang, Jenny Hallberg, Dimitrios Charalampopoulos, Maribel Casas Sanahuja, Robab Breyer-Kohansal, Arnulf Langhammer, Raquel Granell, et al. "Spirometric phenotypes from early childhood to young adulthood: a Chronic Airway Disease Early Stratification study." ERJ Open Research 7, no. 4 (September 29, 2021): 00457–2021. http://dx.doi.org/10.1183/23120541.00457-2021.
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BackgroundThe prevalences of obstructive and restrictive spirometric phenotypes, and their relation to early-life risk factors from childhood to young adulthood remain poorly understood. The aim was to explore these phenotypes and associations with well-known respiratory risk factors across ages and populations in European cohorts.MethodsWe studied 49 334 participants from 14 population-based cohorts in different age groups (≤10, >10–15, >15–20, >20–25 years, and overall, 5–25 years). The obstructive phenotype was defined as forced expiratory volume in 1 s (FEV1)/forced vital capacity (FVC) z-score less than the lower limit of normal (LLN), whereas the restrictive phenotype was defined as FEV1/FVC z-score ≥LLN, and FVC z-score <LLN.ResultsThe prevalence of obstructive and restrictive phenotypes varied from 3.2–10.9% and 1.8–7.7%, respectively, without clear age trends. A diagnosis of asthma (adjusted odds ratio (aOR=2.55, 95% CI 2.14–3.04), preterm birth (aOR=1.84, 1.27–2.66), maternal smoking during pregnancy (aOR=1.16, 95% CI 1.01–1.35) and family history of asthma (aOR=1.44, 95% CI 1.25–1.66) were associated with a higher prevalence of obstructive, but not restrictive, phenotype across ages (5–25 years). A higher current body mass index (BMI was more often observed in those with the obstructive phenotype but less in those with the restrictive phenotype (aOR=1.05, 95% CI 1.03–1.06 and aOR=0.81, 95% CI 0.78–0.85, per kg·m−2 increase in BMI, respectively). Current smoking was associated with the obstructive phenotype in participants older than 10 years (aOR=1.24, 95% CI 1.05–1.46).ConclusionObstructive and restrictive phenotypes were found to be relatively prevalent during childhood, which supports the early origins concept. Several well-known respiratory risk factors were associated with the obstructive phenotype, whereas only low BMI was associated with the restrictive phenotype, suggesting different underlying pathobiology of these two phenotypes.
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Chen, Chen X., Susan Ofner, Giorgos Bakoyannis, Kristine L. Kwekkeboom, and Janet S. Carpenter. "Symptoms-Based Phenotypes Among Women With Dysmenorrhea: A Latent Class Analysis." Western Journal of Nursing Research 40, no. 10 (September 15, 2017): 1452–68. http://dx.doi.org/10.1177/0193945917731778.
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Dysmenorrhea is highly prevalent and may increase women’s risk for developing other chronic pain conditions. Although it is highly variable, symptom-based dysmenorrhea phenotypes have not been identified. The aims of the study were to identify symptom-based dysmenorrhea phenotypes and examine their relationships with demographic and clinical characteristics. In a cross-sectional study, 762 women with dysmenorrhea rated severity of 14 dysmenorrhea-related symptoms. Using latent class analysis, we identified three distinctive phenotypes. Women in the “mild localized pain” phenotype ( n = 202, 26.51%) had mild abdominal cramps and dull abdominal pain/discomfort. Women in the “severe localized pain” phenotype ( n = 412, 54.07%) had severe abdominal cramps. Women in the “multiple severe symptoms” phenotype ( n = 148, 19.42%) had severe pain at multiple locations and multiple gastrointestinal symptoms. Race, ethnicity, age, and comorbid chronic pain conditions were significantly associated with phenotypes. Identification of these symptom-based phenotypes provides a foundation for research examining genotype–phenotype associations, etiologic mechanisms, and/or variability in treatment responses.
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Wu, Bizhi, Hangxiao Zhang, Limei Lin, Huiyuan Wang, Yubang Gao, Liangzhen Zhao, Yi-Ping Phoebe Chen, Riqing Chen, and Lianfeng Gu. "A Similarity Searching System for Biological Phenotype Images Using Deep Convolutional Encoder-decoder Architecture." Current Bioinformatics 14, no. 7 (September 17, 2019): 628–39. http://dx.doi.org/10.2174/1574893614666190204150109.
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Background: The BLAST (Basic Local Alignment Search Tool) algorithm has been widely used for sequence similarity searching. Analogously, the public phenotype images must be efficiently retrieved using biological images as queries and identify the phenotype with high similarity. Due to the accumulation of genotype-phenotype-mapping data, a system of searching for similar phenotypes is not available due to the bottleneck of image processing. Objective: In this study, we focus on the identification of similar query phenotypic images by searching the biological phenotype database, including information about loss-of-function and gain-of-function. Methods: We propose a deep convolutional autoencoder architecture to segment the biological phenotypic images and develop a phenotype retrieval system to enable a better understanding of genotype–phenotype correlation. Results: This study shows how deep convolutional autoencoder architecture can be trained on images from biological phenotypes to achieve state-of-the-art performance in a phenotypic images retrieval system. Conclusion: Taken together, the phenotype analysis system can provide further information on the correlation between genotype and phenotype. Additionally, it is obvious that the neural network model of image segmentation and the phenotype retrieval system is equally suitable for any species, which has enough phenotype images to train the neural network.
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Ahnert, S. E. "Structural properties of genotype–phenotype maps." Journal of The Royal Society Interface 14, no. 132 (July 2017): 20170275. http://dx.doi.org/10.1098/rsif.2017.0275.
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The map between genotype and phenotype is fundamental to biology. Biological information is stored and passed on in the form of genotypes, and expressed in the form of phenotypes. A growing body of literature has examined a wide range of genotype–phenotype (GP) maps and has established a number of properties that appear to be shared by many GP maps. These properties are ‘structural’ in the sense that they are properties of the distribution of phenotypes across the point-mutation network of genotypes. They include: a redundancy of genotypes, meaning that many genotypes map to the same phenotypes, a highly non-uniform distribution of the number of genotypes per phenotype, a high robustness of phenotypes and the ability to reach a large number of new phenotypes within a small number of mutational steps. A further important property is that the robustness and evolvability of phenotypes are positively correlated. In this review, I give an overview of the study of GP maps with particular emphasis on these structural properties, and discuss a model that attempts to explain why these properties arise, as well as some of the fundamental ways in which the structure of GP maps can affect evolutionary outcomes.
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Ahuja, Yuri, Doudou Zhou, Zeling He, Jiehuan Sun, Victor M. Castro, Vivian Gainer, Shawn N. Murphy, Chuan Hong, and Tianxi Cai. "sureLDA: A multidisease automated phenotyping method for the electronic health record." Journal of the American Medical Informatics Association 27, no. 8 (June 17, 2020): 1235–43. http://dx.doi.org/10.1093/jamia/ocaa079.
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Abstract Objective A major bottleneck hindering utilization of electronic health record data for translational research is the lack of precise phenotype labels. Chart review as well as rule-based and supervised phenotyping approaches require laborious expert input, hampering applicability to studies that require many phenotypes to be defined and labeled de novo. Though International Classification of Diseases codes are often used as surrogates for true labels in this setting, these sometimes suffer from poor specificity. We propose a fully automated topic modeling algorithm to simultaneously annotate multiple phenotypes. Materials and Methods Surrogate-guided ensemble latent Dirichlet allocation (sureLDA) is a label-free multidimensional phenotyping method. It first uses the PheNorm algorithm to initialize probabilities based on 2 surrogate features for each target phenotype, and then leverages these probabilities to constrain the LDA topic model to generate phenotype-specific topics. Finally, it combines phenotype-feature counts with surrogates via clustering ensemble to yield final phenotype probabilities. Results sureLDA achieves reliably high accuracy and precision across a range of simulated and real-world phenotypes. Its performance is robust to phenotype prevalence and relative informativeness of surogate vs nonsurrogate features. It also exhibits powerful feature selection properties. Discussion sureLDA combines attractive properties of PheNorm and LDA to achieve high accuracy and precision robust to diverse phenotype characteristics. It offers particular improvement for phenotypes insufficiently captured by a few surrogate features. Moreover, sureLDA’s feature selection ability enables it to handle high feature dimensions and produce interpretable computational phenotypes. Conclusions sureLDA is well suited toward large-scale electronic health record phenotyping for highly multiphenotype applications such as phenome-wide association studies .
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Deng, Lizong, Luming Chen, Tao Yang, Mi Liu, Shicheng Li, and Taijiao Jiang. "Constructing High-Fidelity Phenotype Knowledge Graphs for Infectious Diseases With a Fine-Grained Semantic Information Model: Development and Usability Study." Journal of Medical Internet Research 23, no. 6 (June 15, 2021): e26892. http://dx.doi.org/10.2196/26892.
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Background Phenotypes characterize the clinical manifestations of diseases and provide important information for diagnosis. Therefore, the construction of phenotype knowledge graphs for diseases is valuable to the development of artificial intelligence in medicine. However, phenotype knowledge graphs in current knowledge bases such as WikiData and DBpedia are coarse-grained knowledge graphs because they only consider the core concepts of phenotypes while neglecting the details (attributes) associated with these phenotypes. Objective To characterize the details of disease phenotypes for clinical guidelines, we proposed a fine-grained semantic information model named PhenoSSU (semantic structured unit of phenotypes). Methods PhenoSSU is an “entity-attribute-value” model by its very nature, and it aims to capture the full semantic information underlying phenotype descriptions with a series of attributes and values. A total of 193 clinical guidelines for infectious diseases from Wikipedia were selected as the study corpus, and 12 attributes from SNOMED-CT were introduced into the PhenoSSU model based on the co-occurrences of phenotype concepts and attribute values. The expressive power of the PhenoSSU model was evaluated by analyzing whether PhenoSSU instances could capture the full semantics underlying the descriptions of the corresponding phenotypes. To automatically construct fine-grained phenotype knowledge graphs, a hybrid strategy that first recognized phenotype concepts with the MetaMap tool and then predicted the attribute values of phenotypes with machine learning classifiers was developed. Results Fine-grained phenotype knowledge graphs of 193 infectious diseases were manually constructed with the BRAT annotation tool. A total of 4020 PhenoSSU instances were annotated in these knowledge graphs, and 3757 of them (89.5%) were found to be able to capture the full semantics underlying the descriptions of the corresponding phenotypes listed in clinical guidelines. By comparison, other information models, such as the clinical element model and the HL7 fast health care interoperability resource model, could only capture the full semantics underlying 48.4% (2034/4020) and 21.8% (914/4020) of the descriptions of phenotypes listed in clinical guidelines, respectively. The hybrid strategy achieved an F1-score of 0.732 for the subtask of phenotype concept recognition and an average weighted accuracy of 0.776 for the subtask of attribute value prediction. Conclusions PhenoSSU is an effective information model for the precise representation of phenotype knowledge for clinical guidelines, and machine learning can be used to improve the efficiency of constructing PhenoSSU-based knowledge graphs. Our work will potentially shift the focus of medical knowledge engineering from a coarse-grained level to a more fine-grained level.
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Rak, Dominik, Lukas Klann, Tizian Heinz, Philip Anderson, Ioannis Stratos, Alexander J. Nedopil, and Maximilian Rudert. "Influence of Mechanical Alignment on Functional Knee Phenotypes and Clinical Outcomes in Primary TKA: A 1-Year Prospective Analysis." Journal of Personalized Medicine 13, no. 5 (April 30, 2023): 778. http://dx.doi.org/10.3390/jpm13050778.
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In total knee arthroplasty (TKA), functional knee phenotypes are of interest regarding surgical alignment strategies. Functional knee phenotypes were introduced in 2019 and consist of limb, femoral, and tibial phenotypes. The hypothesis of this study was that mechanically aligned (MA) TKA changes preoperative functional phenotypes, which decreases the 1-year Forgotten Joint (FJS) and Oxford Knee Score (OKS) and increases the 1-year WOMAC. All patients included in this study had end-stage osteoarthritis and were treated with a primary MA TKA, which was supervised by four academic knee arthroplasty specialists. To determine the limb, femoral, and tibial phenotype, a long-leg radiograph (LLR) was imaged preoperatively and two to three days after TKA. FJS, OKS, and WOMAC were obtained 1 year after TKA. Patients were categorized using the change in functional limb, femoral, and tibial phenotype measured on LLR, and the scores were compared between the different categories. A complete dataset of preoperative and postoperative scores and radiographic images could be obtained for 59 patients. 42% of these patients had a change of limb phenotype, 41% a change of femoral phenotype, and 24% a change of tibial phenotype of more than ±1 relative to the preoperative phenotype. Patients with more than ±1 change of limb phenotype had significantly lower median FJS (27 points) and OKS (31 points) and higher WOMAC scores (30 points) relative to the 59-, 41-, and 4-point scores of those with a 0 ± 1 change (p < 0.0001 to 0.0048). Patients with a more than ±1 change of femoral phenotype had significantly lower median FJS (28 points) and OKS (32 points) and higher WOMAC scores (24 points) relative to the 69-, 40-, and 8-point scores of those with a 0 ± 1 change (p < 0.0001). A change in tibial phenotype had no effect on the FJS, OKS, and WOMAC scores. Surgeons performing MA TKA could consider limiting coronal alignment corrections of the limb and femoral joint line to within one phenotype to reduce the risk of low patient-reported satisfaction and function at 1-year.
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Henriksen, Hanne H., Igor Marín de Mas, Lars K. Nielsen, Joseph Krocker, Jakob Stensballe, Sigurður T. Karvelsson, Niels H. Secher, Óttar Rolfsson, Charles E. Wade, and Pär I. Johansson. "Endothelial Cell Phenotypes Demonstrate Different Metabolic Patterns and Predict Mortality in Trauma Patients." International Journal of Molecular Sciences 24, no. 3 (January 23, 2023): 2257. http://dx.doi.org/10.3390/ijms24032257.
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In trauma patients, shock-induced endotheliopathy (SHINE) is associated with a poor prognosis. We have previously identified four metabolic phenotypes in a small cohort of trauma patients (N = 20) and displayed the intracellular metabolic profile of the endothelial cell by integrating quantified plasma metabolomic profiles into a genome-scale metabolic model (iEC-GEM). A retrospective observational study of 99 trauma patients admitted to a Level 1 Trauma Center. Mass spectrometry was conducted on admission samples of plasma metabolites. Quantified metabolites were analyzed by computational network analysis of the iEC-GEM. Four plasma metabolic phenotypes (A–D) were identified, of which phenotype D was associated with an increased injury severity score (p < 0.001); 90% (91.6%) of the patients who died within 72 h possessed this phenotype. The inferred EC metabolic patterns were found to be different between phenotype A and D. Phenotype D was unable to maintain adequate redox homeostasis. We confirm that trauma patients presented four metabolic phenotypes at admission. Phenotype D was associated with increased mortality. Different EC metabolic patterns were identified between phenotypes A and D, and the inability to maintain adequate redox balance may be linked to the high mortality.
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Spiegel, Ronen, Hanna Mandel, Ann Saada, Issy Lerer, Ayala Burger, Avraham Shaag, Stavit A. Shalev, et al. "Delineation of C12orf65-related phenotypes: a genotype–phenotype relationship." European Journal of Human Genetics 22, no. 8 (January 15, 2014): 1019–25. http://dx.doi.org/10.1038/ejhg.2013.284.
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Liang, Jia, Johannes Von den Hoff, Joanna Lange, Yijin Ren, Zhuan Bian, and Carine E. L. Carels. "MSX1 mutations and associated disease phenotypes: genotype-phenotype relations." European Journal of Human Genetics 24, no. 12 (July 6, 2016): 1663–70. http://dx.doi.org/10.1038/ejhg.2016.78.
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Angelone, Steven, Iván Piña-Torres, Israel Padilla-Guerrero, and Michael Bidochka. "“Sleepers” and “Creepers”: A Theoretical Study of Colony Polymorphisms in the Fungus Metarhizium Related to Insect Pathogenicity and Plant Rhizosphere Colonization." Insects 9, no. 3 (August 17, 2018): 104. http://dx.doi.org/10.3390/insects9030104.
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Different strains of Metarhizium exhibit a range of polymorphisms in colony phenotypes. These phenotypes range from highly conidiating colonies to colonies that produce relatively more mycelia and few conidia. These different phenotypes are exhibited in infected insects in the soil. In this paper, we provide a theoretical consideration of colony polymorphisms and suggest that these phenotypes represent a range of strategies in the soil that Metarhizium exhibits. We call these different strategies “sleepers” and “creepers”. The “sleeper” phenotype produces relatively greater amounts of conidia. We use the term “sleeper” to identify this phenotype since this strategy is to remain in the soil as conidia in a relatively metabolically inactive state until a host insect or plant encounter these conidia. The “creeper” phenotype is predominantly a mycelial phenotype. In this strategy, hyphae move through the soil until a host insect or plant is encountered. We theoretically model the costs and benefits of these phenotypic polymorphisms and suggest how evolution could possibly select for these different strategies.
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Choron, Rachel L., Stephen A. Iacono, Alexander Cong, Christopher G. Bargoud, Amanda L. Teichman, Nicole J. Krumrei, Michelle T. Bover Manderski, Michael B. Rodricks, Rajan Gupta, and Matthew E. Lissauer. "The correlation of respiratory system compliance and mortality in COVID-19 acute respiratory distress syndrome: do phenotypes really exist?" Journal of Lung, Pulmonary & Respiratory Research 8, no. 2 (2021): 67–74. http://dx.doi.org/10.15406/jlprr.2021.08.00253.
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Background: Recent literature suggests respiratory system compliance (Crs) based phenotypes exist among COVID-19 ARDS patients. We sought to determine whether these phenotypes exist and whether Crs predicts mortality. Methods: A retrospective observational cohort study of 111 COVID-19 ARDS patients admitted March 11-July 8, 2020. Crs was averaged for the first 72-hours of mechanical ventilation. Crs<30ml/cmH2O was defined as poor Crs(phenotype-H) whereas Crs≥30ml/cmH2O as preserved Crs(phenotype-L). Results: 111 COVID-19 ARDS patients were included, 40 phenotype-H and 71 phenotype-L. Both the mean PaO2/FiO2 ratio for the first 72-hours of mechanical ventilation and the PaO2/FiO2 ratio hospital nadir were lower in phenotype-H than L(115[IQR87] vs 165[87], p=0.016), (63[32] vs 75[59], p=0.026). There were no difference in characteristics, diagnostic studies, or complications between groups. Twenty-seven (67.5%) phenotype-H patients died vs 37(52.1%) phenotype-L(p=0.115). Multivariable regression did not reveal a mortality difference between phenotypes; however, a 2-fold mortality increase was noted in Crs<20 vs >50ml/cmH2O when analyzing ordinal Crs groups. Moving up one group level (ex. Crs30-39.9ml/cmH2O to 40-49.9ml/cmH2O), was marginally associated with 14% lower risk of death(RR=0.86, 95%CI 0.72, 1.01, p=0.065). This attenuated (RR=0.94, 95%CI 0.80, 1.11) when adjusting for pH nadir and PaO2/FiO2 ratio nadir. Conclusion: We identified a spectrum of Crs in COVID-19 ARDS similar to Crs distribution in non-COVID-19 ARDS. While we identified increasing mortality as Crs decreased, there was no specific threshold marking significantly different mortality based on phenotype. We therefore would not define COVID-19 ARDS patients by phenotypes-H or L and would not stray from traditional ARDS ventilator management strategies.
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Kolmer, J. A., D. L. Long, E. Kosman, and M. E. Hughes. "Physiologic Specialization of Puccinia triticina on Wheat in the United States in 2001." Plant Disease 87, no. 7 (July 2003): 859–66. http://dx.doi.org/10.1094/pdis.2003.87.7.859.
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Collections of Puccinia triticina were obtained from rust-infected wheat leaves by cooperators throughout the United States and from surveys of wheat fields and nurseries in the Great Plains, Ohio Valley, Gulf Coast, California, Pacific Northwest, and Atlantic Coast States in order to determine the virulence of the wheat leaf rust fungus in 2001. Single uredinial isolates (477 in total) were derived from the wheat leaf rust collections and tested for virulence phenotype on lines of Thatcher wheat that are near-isogenic for leaf rust resistance genes Lr1, Lr2a, Lr2c, Lr3, Lr9, Lr16, Lr24, Lr26, Lr3ka, Lr11, Lr17, Lr30, LrB, Lr10, Lr14a, and Lr18. The isolates also were tested for virulence on adult plants with leaf rust resistance genes Lr12, Lr13, Lr22a, Lr22b, Lr34, Lr35, and Lr37. In the United States in 2001, 44 virulence phenotypes of P. triticina were found. Virulence phenotype MBDS, which is virulent to resistance gene Lr17, was the most common phenotype in the United States. MBDS was found in the Southeast, Great Plains, and Ohio Valley regions. Virulence phenotype THBJ, which is virulent to Lr16 and Lr26, was the second most common phenotype, and occurred almost exclusively in the north-central Great Plains region. Phenotype MCDS, which is virulent to Lr17 and Lr26, was the third most common phenotype and was found primarily in the Southeast, Ohio Valley, and Great Plains regions. The Southeast and Ohio Valley regions differed from the Great Plains region for predominant virulence phenotypes, which indicate that populations of P. triticina in those areas are not closely connected. The northern and southern areas of the Great Plains region differed for phenotypes with virulence to Lr16; however, the two areas had other phenotypes in common. Virulence to the adult plant resistance genes Lr35 and Lr37 was detected for the first time in North America in the MBDS, MCJS, and MCDS phenotypes.
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Shulaeha, Ummi, Andi Mardiah Tahir, Nusratuddin Abdullah, and Isharyah Sunarno. "Correlation between phenotype and cortisol with anxiety status related to Polycystic Ovary Syndrome (PCOS)." Bali Medical Journal 12, no. 3 (October 12, 2023): 3032–36. http://dx.doi.org/10.15562/bmj.v12i3.4740.
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Link of Video Abstract: https://youtu.be/VpTspeX7fe0 Background: Polycystic Ovary Syndrome (PCOS) is the most common endocrine disorder in women of reproductive age. Symptoms include irregular menstrual cycles, hirsutism, obesity, acne vulgaris, and infertility. PCOS is a stigmatized condition that affects women's identity and mental health, especially anxiety. In addition, increased cortisol is associated with increased anxiety. This study aims to determine the effect of phenotype and cortisol on anxiety status in polycystic ovary syndrome (PCOS) patients. Methods: 40 patients diagnosed with polycystic ovary syndrome (PCOS) aged 18-40 years in Makassar. The Indonesian version of the Hamilton Anxiety Rating Scale (HAM-A) assesses anxiety. A blood sample is taken to check for cortisol (drip blood test). Cortisol levels were measured using the CMIA (Chemiluminescent microparticle Immunoassay) method. Data were analyzed using SPSS version 25.0 for Windows. Results: The prevalence of phenotypes A, B, C, and D were 27.5%, 0%, 45%, and 45%, respectively. Phenotype C had a higher body mass index than the other phenotypes but was not significantly different. (p > 0.05). About 26.1% of patients with phenotype A were found to suffer from mild anxiety and 29.5% experienced moderate anxiety. Compared to phenotype C, 52.2% experienced mild anxiety and 35.5% experienced moderate anxiety; however, these results were not statistically significant. Higher cortisol levels were found in phenotype A compared to other phenotypes (phenotype C; 7.01±3.12 and phenotype D; 6.37±3.02) but not significantly different (p>0.05). Conclusion: The PCOS phenotype has no relationship with the anxiety status of PCOS patients and there is no relationship between the phenotype and serum cortisol levels in PCOS patients.
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Li, Yujia, Yusi Fang, Hung-Ching Chang, Michael Gorczyca, Peng Liu, and George C. Tseng. "Adaptively Integrative Association between Multivariate Phenotypes and Transcriptomic Data for Complex Diseases." Genes 14, no. 4 (March 26, 2023): 798. http://dx.doi.org/10.3390/genes14040798.
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Phenotype–gene association studies can uncover disease mechanisms for translational research. Association with multiple phenotypes or clinical variables in complex diseases has the advantage of increasing statistical power and offering a holistic view. Existing multi-variate association methods mostly focus on SNP-based genetic associations. In this paper, we extend and evaluate two adaptive Fisher’s methods, namely AFp and AFz, from the p-value combination perspective for phenotype–mRNA association analysis. The proposed method effectively aggregates heterogeneous phenotype–gene effects, allows association with different data types of phenotypes, and performs the selection of the associated phenotypes. Variability indices of the phenotype–gene effect selection are calculated by bootstrap analysis, and the resulting co-membership matrix identifies gene modules clustered by phenotype–gene effect. Extensive simulations demonstrate the superior performance of AFp compared to existing methods in terms of type I error control, statistical power and biological interpretation. Finally, the method is separately applied to three sets of transcriptomic and clinical datasets from lung disease, breast cancer, and brain aging and generates intriguing biological findings.
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Ruslyakova, I. A., E. Z. Shamsutdinova, and L. B. Gaikovaya. "Relationship Between Sepsis Phenotypes and Treatment Characteristics of Patients with Viral and Bacterial Pneumonia." General Reanimatology 20, no. 2 (April 24, 2024): 29–39. http://dx.doi.org/10.15360/1813-9779-2024-2-29-40.
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New subgroups of patients with severe community-acquired pneumonia (SCAP) are hardly predicted by the use of clinical covariates; clusterization may significantly improve diagnostic approaches and facilitate the adaptation of specific treatment modalities to patient’s individual characteristics.The aim of the study. To identify linking the sepsis phenotype in patients with SCAP and preferable treatment option to forecasting the outcome and improve treatment results.Materials and methods. Case histories of 664 of intensive care unit (ICU) patients with sepsis (2016–2023) from I. I. Mechnikov Northwestern State Medical University were analyzed. The study included 568 (85.5%) patients with viral SCAP (SCAPv group) and 96 (14.5%) patients with bacterial SCAP (SCAPb group). Sepsis phenotypes were identified using algorithm proposed by Seymour C.W. et al. In SCAP cases associated with COVID-19 infection (n=293, 51.6%) patients received genetically engineered biological therapy (GIBT). The study compared two cohorts of patients: those who received GIBT and did not receive GIBT. Data were statistically processed using the Statistica 10.0 and SPSS software packages.Results. Analysis revealed 4 sepsis phenotypes: α- (N=323, 48.6%); β- (N=128, 19.3%); γ- (N=87, 13.1%); δ - (N=126, 19%). The majority of SCAPv group patients — 295 (51.9%) — had α-phenotype of sepsis, while δ -phenotype prevailed in the SCAPb group — 53 (55.2%). The proportion of patients receiving GIBT and exhibiting α- sepsis phenotype dominated over other sepsis phenotypes: 61.8% of patientspossesed α- phenotype, whereas β-, γ- and δ -phenotypes were determined in 16% , 12.6%, and 9.6% of GIBT patients, respectivelty (P<0.05). The best effect of using monoclonal antibodies to interleukin-6 receptors as a GIBT was obtained in patients with the α-phenotype sepsis and COVID-19-associated SCAP: 87.5% favorable outcomes, P=0.0419. Rate of bacterial sepsis was significantly lower in patients with α- and δ -phenotypes of sepsis receiving GIBT vs those who did not receive this therapy: 12.71% vs 23.2% of patients with α-phenotype, P=0.0131; 25.0% vs 70.41% of patients with δ -phenotype, P=0.0254, respectively.Conclusion. Differences in sepsis phenotype between patients with viral or bacterial SCAP may stratify patients for different therapeutic management and more accurately predict potential complications and unfavorable outcome.
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Jin, Xiaoqin, and Gang Shi. "Kernel-based gene–environment interaction tests for rare variants with multiple quantitative phenotypes." PLOS ONE 17, no. 10 (October 12, 2022): e0275929. http://dx.doi.org/10.1371/journal.pone.0275929.
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Previous studies have suggested that gene–environment interactions (GEIs) between a common variant and an environmental factor can influence multiple correlated phenotypes simultaneously, that is, GEI pleiotropy, and that analyzing multiple phenotypes jointly is more powerful than analyzing phenotypes separately by using single-phenotype GEI tests. Methods to test the GEI for rare variants with multiple phenotypes are, however, lacking. In our work, we model the correlation among the GEI effects of a variant on multiple quantitative phenotypes through four kernels and propose four multiphenotype GEI tests for rare variants, which are a test with a homogeneous kernel (Hom-GEI), a test with a heterogeneous kernel (Het-GEI), a test with a projection phenotype kernel (PPK-GEI) and a test with a linear phenotype kernel (LPK-GEI). Through numerical simulations, we show that correlation among phenotypes can enhance the statistical power except for LPK-GEI, which simply combines statistics from single-phenotype GEI tests and ignores the phenotypic correlations. Among almost all considered scenarios, Het-GEI and PPK-GEI are more powerful than Hom-GEI and LPK-GEI. We apply Het-GEI and PPK-GEI in the genome-wide GEI analysis of systolic blood pressure (SBP) and diastolic blood pressure (DBP) in the UK Biobank. We analyze 18,101 genes and find that LEUTX is associated with SBP and DBP (p = 2.20×10−6) through its interaction with hemoglobin. The single-phenotype GEI test and our multiphenotype GEI tests Het-GEI and PPK-GEI are also used to evaluate the gene–hemoglobin interactions for 22 genes that were previously reported to be associated with SBP or DBP in a meta-analysis of genetic main effects. MYO1C shows nominal significance (p < 0.05) by the Het-GEI test. NOS3 shows nominal significance in DBP and MYO1C in both SBP and DBP by the single-phenotype GEI test.
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Kolmer, J. A., D. L. Long, and M. E. Hughes. "Physiologic Specialization of Puccinia triticina on Wheat in the United States in 2002." Plant Disease 88, no. 10 (October 2004): 1079–84. http://dx.doi.org/10.1094/pdis.2004.88.10.1079.
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Collections of Puccinia triticina were obtained from rust-infected wheat leaves by cooperators throughout the United States and from surveys of wheat fields and nurseries in the Great Plains, Ohio Valley, Southeast, California, and the Pacific Northwest, in order to determine the virulence of the wheat leaf rust fungus in 2002. Single uredinial isolates (785 in total) were derived from the wheat leaf rust collections and tested for virulence phenotype on lines of Thatcher wheat that are near-isogenic for leaf rust resistance genes Lr1, Lr2a, Lr2c, Lr3, Lr9, Lr16, Lr24, Lr26, Lr3ka, Lr11, Lr17, Lr30, LrB, Lr10, Lr14a, and Lr18. In the United States in 2002, 52 virulence phenotypes of P. triticina were found. Virulence phenotype MBDS, which is virulent to resistance gene Lr17, was the most common phenotype in the United States. MBDS was found in the Southeast, Great Plains, and the Ohio Valley regions, and also in California. Phenotype MCDS, virulent to Lr17 and Lr26, was the second most common phenotype and occurred in the same regions as MBDS. Virulence phenotype THBJ, which is virulent to Lr16 and Lr26, was the third most common phenotype, and was found in the southern and northern central Great Plains region. Phenotype TLGJ, with virulence to Lr2a, Lr9, and Lr11, was the fourth most common phenotype and was found primarily in the Southeast and Ohio Valley regions. The Southeast and Ohio Valley regions differed from the Great Plains regions for predominant virulence phenotypes, which indicate that populations of P. triticina in those areas are not closely connected. The northern and southern areas of the Great Plains were similar for frequencies of predominant phenotypes, indicating a strong south to north migration of urediniospores.
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Wei, Xinzhu, and Jianzhi Zhang. "Why Phenotype Robustness Promotes Phenotype Evolvability." Genome Biology and Evolution 9, no. 12 (December 1, 2017): 3509–15. http://dx.doi.org/10.1093/gbe/evx264.
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Abdelhafiz, Ahmed H., Grace L. Keegan, and Alan J. Sinclair. "Metabolic Characteristics of Frail Older People with Diabetes Mellitus—A Systematic Search for Phenotypes." Metabolites 13, no. 6 (May 29, 2023): 705. http://dx.doi.org/10.3390/metabo13060705.
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Frailty in older people with diabetes is viewed as one homogeneous category. We previously suggested that frailty is not homogeneous and spans across a metabolic spectrum that starts with an anorexic malnourished (AM) frail phenotype and ends with a sarcopenic obese (SO) phenotype. We aimed to investigate the metabolic characteristics of frail older people with diabetes reported in the current literature to explore whether they fit into two distinctive metabolic phenotypes. We performed systematic review of studies published over the last 10 years and reported characteristics of frail older people with diabetes mellitus. A total of 25 studies were included in this systematic review. Fifteen studies reported frail patients’ characteristics that could fit into an AM phenotype. This phenotype is characterised by low body weight, increased prevalence of malnutrition markers such as low serum albumin, low serum cholesterol, low Hb, low HbA1c, and increased risk of hypoglycaemia. Ten studies reported frail patients’ characteristics that describe a SO phenotype. This phenotype is characterised by increased body weight, increased serum cholesterol, high HbA1c, and increased blood glucose levels. Due to significant weight loss in the AM phenotype, insulin resistance decreases, leading to a decelerated diabetes trajectory and reduced hypoglycaemic agent use or deintensification of therapy. On the other hand, in the SO phenotype, insulin resistance increases leading to accelerated diabetes trajectory and increased hypoglycaemic agent use or intensification of therapy. Current literature suggests that frailty is a metabolically heterogeneous condition that includes AM and SO phenotypes. Both phenotypes have metabolically distinctive features, which will have a different effect on diabetes trajectory. Therefore, clinical decision-making and future clinical studies should consider the metabolic heterogeneity of frailty.
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Fernandes, S. R., I. Rodrigues, S. Saraiva, S. Bernardo, A. Rita Gonçalves, P. Moura Santos, A. Valente, L. Correia, H. Cortez-Pinto, and F. Magro. "P443 Transmural Remission Associates with a Lower Risk of Phenotype Progression in Crohn’s Disease." Journal of Crohn's and Colitis 18, Supplement_1 (January 1, 2024): i904—i905. http://dx.doi.org/10.1093/ecco-jcc/jjad212.0573.
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Abstract Background Patients with Crohn’s disease (CD) are at risk of progressing from inflammatory to stricturing and penetrating phenotypes. The influence of the type of remission on phenotype progression has not been adequately evaluated. Methods Retrospective cohort study including surgically naïve CD patients with inflammatory or stricturing phenotype evaluated concomitantly by magnetic resonance enterography and colonoscopy. The degree of remission was correlated with the risk of progressing to stricturing and penetrating phenotypes. Results 381 CD patients were included, 21.8% with transmural remission, 21.3% with isolated endoscopic remission, 10.5% with isolated radiologic remission, and 46.5% without remission. Patients with transmural remission presented the lowest rates of phenotype progression (1.2%), with a significant difference compared to isolated endoscopic remission (18.3%, P≤ 0.001), isolated radiologic remission (17.5%, P=0.002), and no remission (36.9%, P≤0.001). In multivariate regression analysis, transmural remission (OR 0.023 95%CI 0.003-0.171, P<0.001), isolated radiologic remission (OR 0.342 95%CI 0.141-0.826, P=0.017), and isolated endoscopic remission (OR 0.441 95%CI 0.220-0.881, P=0.020) resulted in lower rates of phenotype progression compared to no remission. Conclusion The degree of intestinal remission correlates with the risk of phenotype progression. Patients with transmural remission are at the lowest risk of progressing to stricturing and penetrating phenotypes. Figure 1 presents the Kaplan-Meier estimates of remaining free of penetrating complication (upper curve) and free of stricturing and/or penetrating complication (lower curve) after the baseline assessment in patients with no remission (A), isolated endoscopic remission (B), isolated radiologic remission (C), and transmural remission (D). B1, non-stricturing non-penetrating phenotype; B2, stricturing phenotype; B3, penetrating phenotype.
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Abashova, Elena I., Maria I. Yarmolinskaya, Olga L. Bulgakova, and Elena V. Misharina. "Lipid profile in women of reproductive age with various polycystic ovary syndrome phenotypes." Journal of obstetrics and women's diseases 69, no. 6 (January 25, 2021): 7–16. http://dx.doi.org/10.17816/jowd6967-16.
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Hypothesis/Aims of study. Dyslipidemia is a common metabolic disorder and is an atherogenic factor in the development of cardiovascular disease in women with polycystic ovary syndrome. Currently, four phenotypes of polycystic ovary syndrome are distinguished, associated in varying degrees of severity with dyslipidemia, insulin resistance, impaired glucose tolerance, and diabetes mellitus on one hand and chronic inflammation and oxidative stress on the other. Hyperandrogenic phenotypes (A, B, C) in polycystic ovary syndrome are associated with the development of adverse metabolic disorders and associated complications. The aim of this study was to evaluate the lipid profile in the serum of women of reproductive age with various polycystic ovary syndrome phenotypes.
Study design, materials and methods. The study included 86 women of reproductive age from 22 to 37 years old (average age was 26.6 4.3 years), who, in accordance with polycystic ovary syndrome phenotypes (A, B, C, D), were divided into four groups. We studied the levels of anti-Mllerian hormone, follicle-stimulating and luteinizing hormones, prolactin, estradiol, and androgens from days 2 to 5 of the menstrual cycle. The levels of progesterone in the blood serum were determined by the enzyme immunoassay on days 20 to 23 of the menstrual cycle for three consecutive cycles. We also used echographic methods for diagnosing polycystic ovaries. All women underwent a biochemical blood test with an assessment of the lipid profile parameters (total cholesterol, triglycerides, high-density lipoproteins (HDL), and low-density lipoproteins, LDL). Besides, an oral glucose tolerance test was assessed with the study of plasma glucose and insulin levels on an empty stomach and two hours after ingestion of 75 g of glucose, the HOMA-IR index being used to assess insulin resistance.
Results. Phenotype A was found in 40 (46.5%) women with polycystic ovary syndrome, phenotype B in 22 (25.6%), phenotype C in 10 (11.6%), and phenotype D (non-androgenic) in 14 (16.3%) patients with PCOS. Of those 42 (48.8%) individuals had changes in carbohydrate metabolism (impaired glucose tolerance), of whom 39 (92.8%) women had androgenic polycystic ovary syndrome phenotypes (A, B, C). Both non-androgenic phenotype D and impaired glucose tolerance were found in 7.2% of cases. In women with hyperandrogenic polycystic ovary syndrome phenotypes, both the fasting and stimulated insulin levels were increased significantly comparing to the non-androgenic anovulatory phenotype (p 0.05). The HOMA-IR index in women with phenotypes A, B and C was significantly (p 0.05) higher than in patients with non-androgenic phenotype D. When evaluating the lipid profile parameters, no significant differences in cholesterol level and atherogenic coefficient in women with various polycystic ovary syndrome phenotypes were found. The levels of triglycerides and LDL were significantly (p 0.05) higher in women with androgenic phenotype B compared to those in patients with non-androgenic phenotype D and they correlated significantly (p 0.05) with the serum levels of androgens and sex hormone-binding globulin (SHBG). Patients with androgenic polycystic ovary syndrome phenotypes (A and B) had significantly (p 0.05) decreased HDL levels that correlated negatively (r = 0.29; p 0.05) with the levels of free testosterone and SHBG, when compared to the same parameters in women with non-androgenic phenotype D. In women with androgenic polycystic ovary syndrome phenotypes (A, B, C), a significant correlation (r = 0.27; p 0.05) between the levels of stimulated insulin and SHBG were found, and a direct relation (r = 0.32; p 0.05) between those parameters and increased levels of triglycerides and LDL was also revealed.
Conclusion. In women with hyperandrogenic and anovulatory polycystic ovary syndrome phenotypes A and B, atherogenic dyslipidemia and impaired carbohydrate metabolism were significantly more pronounced, when compared with patients with non-androgenic phenotype D. A differential and personalized approach to the examination of patients with various polycystic ovary syndrome phenotypes is an important step in the prevention of the risks of developing cardiovascular diseases in women of reproductive age.
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Rajpurohit, Subhash, Rani Richardson, John Dean, Raul Vazquez, Grace Wong, and Paul S. Schmidt. "Pigmentation and fitness trade-offs through the lens of artificial selection." Biology Letters 12, no. 10 (October 2016): 20160625. http://dx.doi.org/10.1098/rsbl.2016.0625.
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Pigmentation is a classic phenotype that varies widely and adaptively in nature both within and among taxa. Genes underlying pigmentation phenotype are highly pleiotropic, creating the potential for functional trade-offs. However, the basic tenets of this trade-off hypothesis with respect to life-history traits have not been directly addressed. In natural populations of Drosophila melanogaster , the degree of melanin pigmentation covaries with fecundity and several other fitness traits. To examine correlations and potential trade-offs associated with variation in pigmentation, we selected replicate outbred populations for extreme pigmentation phenotypes. Replicate populations responded rapidly to the selection regime and after 100 generations of artificial selection were phenotyped for pigmentation as well as the two basic fitness parameters of fecundity and longevity. Our data demonstrate that selection on pigmentation resulted in a significant shift in both fecundity and longevity profiles. Selection for dark pigmentation resulted in greater fecundity and no pronounced change in longevity, whereas selection for light pigmentation decreased longevity but did not affect fecundity. Our results indicate the pleiotropic nature of alleles underlying pigmentation phenotype and elucidate possible trade-offs between pigmentation and fitness traits that may shape patterns of phenotypic variation in natural populations.
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Monés, Jordi, and Marc Biarnés. "Geographic atrophy phenotype identification by cluster analysis." British Journal of Ophthalmology 102, no. 3 (July 20, 2017): 388–92. http://dx.doi.org/10.1136/bjophthalmol-2017-310268.
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Background/aimsTo identify ocular phenotypes in patients with geographic atrophy secondary to age-related macular degeneration (GA) using a data-driven cluster analysis.MethodsThis was a retrospective analysis of data from a prospective, natural history study of patients with GA who were followed for ≥6 months. Cluster analysis was used to identify subgroups within the population based on the presence of several phenotypic features: soft drusen, reticular pseudodrusen (RPD), primary foveal atrophy, increased fundus autofluorescence (FAF), greyish FAF appearance and subfoveal choroidal thickness (SFCT). A comparison of features between the subgroups was conducted, and a qualitative description of the new phenotypes was proposed. The atrophy growth rate between phenotypes was then compared.ResultsData were analysed from 77 eyes of 77 patients with GA. Cluster analysis identified three groups: phenotype 1 was characterised by high soft drusen load, foveal atrophy and slow growth; phenotype 3 showed high RPD load, extrafoveal and greyish FAF appearance and thin SFCT; the characteristics of phenotype 2 were midway between phenotypes 1 and 3. Phenotypes differed in all measured features (p≤0.013), with decreases in the presence of soft drusen, foveal atrophy and SFCT seen from phenotypes 1 to 3 and corresponding increases in high RPD load, high FAF and greyish FAF appearance. Atrophy growth rate differed between phenotypes 1, 2 and 3 (0.63, 1.91 and 1.73 mm2/year, respectively, p=0.0005).ConclusionCluster analysis identified three distinct phenotypes in GA. One of them showed a particularly slow growth pattern.
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Freitag-Wolf, Sandra, Jonas C. Schupp, Björn C. Frye, Annegret Fischer, Raihanatul Anwar, Robert Kieszko, Violeta Mihailović-Vučinić, et al. "Genetic and geographic influence on phenotypic variation in European sarcoidosis patients." Frontiers in Medicine 10 (August 9, 2023). http://dx.doi.org/10.3389/fmed.2023.1218106.
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IntroductionSarcoidosis is a highly variable disease in terms of organ involvement, type of onset and course. Associations of genetic polymorphisms with sarcoidosis phenotypes have been observed and suggest genetic signatures.MethodsAfter obtaining a positive vote of the competent ethics committee we genotyped 1909 patients of the deeply phenotyped Genetic-Phenotype Relationship in Sarcoidosis (GenPhenReSa) cohort of 31 European centers in 12 countries with 116 potentially disease-relevant single-nucleotide polymorphisms (SNPs). Using a meta-analysis, we investigated the association of relevant phenotypes (acute vs. sub-acute onset, phenotypes of organ involvement, specific organ involvements, and specific symptoms) with genetic markers. Subgroups were built on the basis of geographical, clinical and hospital provision considerations.ResultsIn the meta-analysis of the full cohort, there was no significant genetic association with any considered phenotype after correcting for multiple testing. In the largest sub-cohort (Serbia), we confirmed the known association of acute onset with TNF and reported a new association of acute onset an HLA polymorphism. Multi-locus models with sets of three SNPs in different genes showed strong associations with the acute onset phenotype in Serbia and Lublin (Poland) demonstrating potential region-specific genetic links with clinical features, including recently described phenotypes of organ involvement.DiscussionThe observed associations between genetic variants and sarcoidosis phenotypes in subgroups suggest that gene–environment-interactions may influence the clinical phenotype. In addition, we show that two different sets of genetic variants are permissive for the same phenotype of acute disease only in two geographic subcohorts pointing to interactions of genetic signatures with different local environmental factors. Our results represent an important step towards understanding the genetic architecture of sarcoidosis.
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Young, Marcus, Natasha E. Holmes, Kartik Kishore, Sobia Amjad, Michele Gaca, Ary Serpa Neto, Michael C. Reade, and Rinaldo Bellomo. "Natural language processing diagnosed behavioural disturbance phenotypes in the intensive care unit: characteristics, prevalence, trajectory, treatment, and outcomes." Critical Care 27, no. 1 (November 4, 2023). http://dx.doi.org/10.1186/s13054-023-04695-0.
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Abstract Background Natural language processing (NLP) may help evaluate the characteristics, prevalence, trajectory, treatment, and outcomes of behavioural disturbance phenotypes in critically ill patients. Methods We obtained electronic clinical notes, demographic information, outcomes, and treatment data from three medical-surgical ICUs. Using NLP, we screened for behavioural disturbance phenotypes based on words suggestive of an agitated state, a non-agitated state, or a combination of both. Results We studied 2931 patients. Of these, 225 (7.7%) were NLP-Dx-BD positive for the agitated phenotype, 544 (18.6%) for the non-agitated phenotype and 667 (22.7%) for the combined phenotype. Patients with these phenotypes carried multiple clinical baseline differences. On time-dependent multivariable analysis to compensate for immortal time bias and after adjustment for key outcome predictors, agitated phenotype patients were more likely to receive antipsychotic medications (odds ratio [OR] 1.84, 1.35–2.51, p < 0.001) compared to non-agitated phenotype patients but not compared to combined phenotype patients (OR 1.27, 0.86–1.89, p = 0.229). Moreover, agitated phenotype patients were more likely to die than other phenotypes patients (OR 1.57, 1.10–2.25, p = 0.012 vs non-agitated phenotype; OR 4.61, 2.14–9.90, p < 0.001 vs. combined phenotype). This association was strongest in patients receiving mechanical ventilation when compared with the combined phenotype (OR 7.03, 2.07–23.79, p = 0.002). A similar increased risk was also seen for patients with the non-agitated phenotype compared with the combined phenotype (OR 6.10, 1.80–20.64, p = 0.004). Conclusions NLP-Dx-BD screening enabled identification of three behavioural disturbance phenotypes with different characteristics, prevalence, trajectory, treatment, and outcome. Such phenotype identification appears relevant to prognostication and trial design. Graphical abstract
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Romero-González, Gregorio A., Maria Lanau, Jordi Soler, Fredzzia Amada Graterol Torres, Nestor Rodriguez-Chitiva, Itziar Castaño, Josep Riera, Marina Urrutia, Joaquín Manrique, and Jordi Bover. "#2875 CONGESTION PHENOTYPES AND DECONGESTION PATTERNS ASSESSED BY POCUS IN HAEMODIALYSIS PATIENTS: A PILOT STUDY." Nephrology Dialysis Transplantation 38, Supplement_1 (June 2023). http://dx.doi.org/10.1093/ndt/gfad063c_2875.
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Abstract Background and Aims Despite the evolution of hemodialysis (HD) in recent years, cardiovascular diseases continue to be the main cause of death in this group of patients. Congestion is one of the factors associated with the highest morbidity and mortality. Unfortunately, there is no gold standard for the proper assessment of dry weight; in fact, physical examination, and even newer techniques such as bioimpedance have poor sensitivity, are difficult to interpret in some cases and fail to discriminate between tissular and vascular congestion. Recently, the use of Point-of-care Ultrasonography (PoCUS) has emerged as the fifth pillar of the conventional physical examination that allows dynamic assessment of congestion and enables congestion to be phenotyped. The aims of the present study were: 1. to phenotype congestion in HD patients and 2. to establish patterns of decongestion according to changes in phenotypes over the course of a HD session. Method Descriptive study carried out in HD units of two tertiary hospitals. Patients with more than three months on HD were included. Excluding patients with short life expectancy or who had an acute complication. In addition to the congestive composite score (CCS), a PoCUS evaluation was performed at the beginning and the end of the HD session on long dialysis interval. Tissular congestion was assessed by lung ultrasound and vascular congestion was assessed by evaluation of the inferior vena cava diameter and portal vein pulsation by pulsed Doppler. Four phenotypes of congestion were established: phenotype A: no congestion, phenotype B: predominance of tissue congestion, phenotype C: predominance of vascular congestion and phenotype D: mixed congestion. In addition, 4 patterns of decongestion were established: pattern 1: absence of congestion at the beginning and end of the HD session, pattern 2: change of phenotype from B, C or D to A, pattern 3: change in congestion phenotype and pattern 4: persistence in the same congestion phenotype as at the beginning. Results 20 patients were included, mean age: 70.5±11.3 years, 14 (70%) were male, CCS: 3 (1.5 – 7.5), interdialysis weight gain: 2.93±0.95 kg and mean UF: 2,570±868mL. 75% of patients reached the prescribed dry weight. At the beginning of the session the distribution of phenotypes was: phenotype A: 35%, phenotype B: 25%, phenotype C: 25% and phenotype D: 15%. At the end of the HD session: phenotype A: 55%, phenotype B: 30%, phenotype C: 10% and phenotype D: 5%. At the end of the HD session 45% of the patients persisted with ultrasound congestion. As for the patterns of decongestion: pattern 1: 35% of patients, pattern 2: 10%, pattern 3: 10% and pattern 4: 35%. 45% of patients persisted with some phenotype of congestion at the end of the session. An inverse correlation was found between age and phenotype at baseline (Rho: -0.506; p = 0.023). No correlation was found between dry weight and UF with decongestion patterns. Conclusion These results show the persistence of echographic congestion at the end of the HD session in a significant number of patients, suggesting that subclinical congestion is more frequent than clinically observed. Further studies are warranted to confirm these results.
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Sotomi, Yohei, Shunsuke Tamaki, Shungo Hikoso, Daisaku Nakatani, Katsuki Okada, Tomoharu Dohi, Akihiro Sunaga, et al. "Pathophysiological insights into machine learning-based subphenotypes of acute heart failure with preserved ejection fraction." Heart, October 12, 2023, heartjnl—2023–323059. http://dx.doi.org/10.1136/heartjnl-2023-323059.
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ObjectiveThe heterogeneous pathophysiology of the diverse heart failure with preserved ejection fraction (HFpEF) phenotypes needs to be examined. We aim to assess differences in the biomarkers among the phenotypes of HFpEF and investigate its multifactorial pathophysiology.MethodsThis study is a retrospective analysis of the PURSUIT-HFpEF Study (N=1231), an ongoing, prospective, multicentre observational study of acute decompensated HFpEF. In this registry, there is a predefined subcohort in which we perform multibiomarker tests (N=212). We applied the previously established machine learning-based clustering model to the subcohort with biomarker measurements to classify them into four phenotypes: phenotype 1 (n=69), phenotype 2 (n=49), phenotype 3 (n=41) and phenotype 4 (n=53). Biomarker characteristics in each phenotype were evaluated.ResultsPhenotype 1 presented the lowest value of N-terminal pro-brain natriuretic peptide (NT-proBNP), high-sensitive C reactive protein, tumour necrosis factor-α, growth differentiation factor (GDF)-15, troponin T and cystatin C, whereas phenotype 2, which is characterised by hypertension and cardiac hypertrophy, showed the highest value of these markers. Phenotype 3 showed the second highest value of GDF-15 and cystatin C. Phenotype 4 presented a low NT-proBNP value and a relatively high GDF-15.ConclusionsDistinctive characteristics of biomarkers in HFpEF phenotypes would indicate differential underlying mechanisms to be elucidated. The contribution of inflammation to the pathogenesis varied considerably among different HFpEF phenotypes. Systemic inflammation substantially contributes to the pathophysiology of the classic HFpEF phenotype with cardiac hypertrophy.Trial registration numberUMIN-CTR ID: UMIN000021831.
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Shen, Yanfei, Dechang Chen, Xinmei Huang, Guolong Cai, Qianghong Xu, Caibao Hu, Jing Yan, and Jiao Liu. "Novel phenotypes of coronavirus disease: a temperature-based trajectory model." Annals of Intensive Care 11, no. 1 (August 3, 2021). http://dx.doi.org/10.1186/s13613-021-00907-4.
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Abstract Background Coronavirus disease has heterogeneous clinical features; however, the reasons for the heterogeneity are poorly understood. This study aimed to identify clinical phenotypes according to patients’ temperature trajectory. Method A retrospective review was conducted in five tertiary hospitals in Hubei Province from November 2019 to March 2020. We explored potential temperature-based trajectory phenotypes and assessed patients’ clinical outcomes, inflammatory response, and response to immunotherapy according to phenotypes. Results A total of 1580 patients were included. Four temperature-based trajectory phenotypes were identified: normothermic (Phenotype 1); fever, rapid defervescence (Phenotype 2); gradual fever onset (Phenotype 3); and fever, slow defervescence (Phenotype 4). Compared with Phenotypes 1 and 2, Phenotypes 3 and 4 had a significantly higher C-reactive protein level and neutrophil count and a significantly lower lymphocyte count. After adjusting for confounders, Phenotypes 3 and 4 had higher in-hospital mortality (adjusted odds ratio and 95% confidence interval 2.1, 1.1–4.0; and 3.3, 1.4–8.2, respectively), while Phenotype 2 had similar mortality, compared with Phenotype 1. Corticosteroid use was associated with significantly higher in-hospital mortality in Phenotypes 1 and 2, but not in Phenotypes 3 or 4 (p for interaction < 0.01). A similar trend was observed for gamma-globulin. Conclusions Patients with different temperature-trajectory phenotypes had different inflammatory responses, clinical outcomes, and responses to corticosteroid therapy.
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Liu, Xinhua, Ling Gao, Yonglin Peng, Zhonghai Fang, and Ju Wang. "PheSom: a term frequency-based method for measuring human phenotype similarity on the basis of MeSH vocabulary." Frontiers in Genetics 14 (July 11, 2023). http://dx.doi.org/10.3389/fgene.2023.1185790.
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Background: Phenotype similarity calculation should be used to help improve drug repurposing. In this study, based on the MeSH terms describing the phenotypes deposited in OMIM, we proposed a method, namely, PheSom (Phenotype Similarity On MeSH), to measure the similarity between phenotypes. PheSom counted the number of overlapping MeSH terms between two phenotypes and then took the weight of every MeSH term within each phenotype into account according to the term frequency-inverse document frequency (FIDC). Phenotype-related genes were used for the evaluation of our method.Results: A 7,739 × 7,739 similarity score matrix was finally obtained and the number of phenotype pairs was dramatically decreased with the increase of similarity score. Besides, the overlapping rates of phenotype-related genes were remarkably increased with the increase of similarity score between phenotypes, which supports the reliability of our method.Conclusion: We anticipate our method can be applied to identifying novel therapeutic methods for complex diseases.